R-ODAF: Omics data analysis framework for regulatory application

• Published in: Regulatory toxicology and pharmacology Vol. 131; p. 105143
• Main Authors: Verheijen, Marcha CT, Meier, Matthew J., Asensio, Juan Ochoteco, Gant, Timothy W., Tong, Weida, Yauk, Carole L., Caiment, Florian
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.06.2022
• Subjects: Data Analysis; DEGs; High-Throughput Nucleotide Sequencing - methods; Reproducibility of Results; RNA-Seq; Sequence Analysis, RNA; Software; Statistical analysis; Statistics; Transcriptomics; Data analysis; DEGs; Statistical analysis; Transcriptomics; RNA-Seq; Statistics
• DOI: 10.1016/j.yrtph.2022.105143
• ISSN: 0273-2300; 1096-0295

Despite the widespread use of transcriptomics technologies in toxicology research, acceptance of the data by regulatory agencies to support the hazard assessment is still limited. Fundamental issues contributing to this are the lack of reproducibility in transcriptomics data analysis arising from variance in the methods used to generate data and differences in the data processing. While research applications are flexible in the way the data are generated and interpreted, this is not the case for regulatory applications where an unambiguous answer, possibly later subject to legal scrutiny, is required. A reference analysis framework would give greater credibility to the data and allow the practitioners to justify their use of an alternative bioinformatic process by referring to a standard. In this publication, we propose a method called omics data analysis framework for regulatory application (R-ODAF), which has been built as a user-friendly pipeline to analyze raw transcriptomics data from microarray and next-generation sequencing. In the R-ODAF, we also propose additional statistical steps to remove the number of false positives obtained from standard data analysis pipelines for RNA-sequencing. We illustrate the added value of R-ODAF, compared to a standard workflow, using a typical toxicogenomics dataset of hepatocytes exposed to paracetamol. •Establish an omics data analysis framework for transcriptomics analysis for regulatory agencies.•Allow the analysis of microarray and RNA-Sequencing data, from raw data files to a list of differentially expressed genes.•Set up innovative filtering criteria to produce a stringent list of differently expressed genes minimizing false positives.•R-ODAF can be used as a reference analysis framework to compare two different datasets.